Anomaly detection system and anomaly detection method

Abstract

The purpose of the present invention is to, when there is an abnormality in an electric vehicle equipped with a drive system which uses a storage battery device, identify not only the abnormality location but also the cause of the abnormality, and easily acquire an abnormality countermeasure proposal including changes to the operation method of the electric vehicle.　The abnormality countermeasure system according to the present invention can be used in an electric vehicle equipped with a drive system which uses a storage battery device. A cause determination unit monitors the state of the electric vehicle and the storage battery device, and, when an abnormality flag has been outputted from at least one of the electric vehicle and the storage battery device, analyzes the state of at least one of the electric vehicle and the storage battery device to identify the cause of the abnormality indicated by the abnormality flag. A countermeasure proposal unit notifies a manager of the electric vehicle about the countermeasure proposal and the cause analysis results of the abnormality flag. The countermeasure proposal includes the following categories: changes to the operation method of the electric vehicle, changes to the control parameters of the storage battery device or the drive system, repairs to faulty software or faulty equipment in the storage battery device or the drive system, and replacement of a degraded storage battery.

Description

Technical Field

[0001] The present invention relates to an abnormality countermeasure system used for an electric vehicle equipped with a storage battery.

Background Art

[0002] Railroad lines have electrified sections where railway vehicles can receive power supply from overhead lines, and non-electrified sections where there are no overhead lines and power supply cannot be received. In conventional railway technology, vehicles ran on diesel engines in non-electrified sections. In recent years, due to the progress of lithium-ion battery technology, vehicles using a storage battery as an energy source, such as hybrid pneumatic vehicles and storage battery trains, are running. A hybrid pneumatic vehicle is a vehicle that mounts a storage battery system on a conventional diesel pneumatic vehicle, charges regenerative power during braking, and performs assist by the storage battery and motor during power running. A storage battery train is a railroad vehicle that mounts a chargeable storage battery system and uses it as a driving energy source. In an electrified section, it receives power supply from an overhead line, charges the storage battery system while using it for driving energy, and runs on the storage battery as an energy source in a non-electrified section.

[0003] The storage batteries used for driving such railroad vehicles are generally used under high load, and abnormalities may occur where the state of the battery goes out of the specification range. Typical abnormalities of the storage battery include over-temperature, over-voltage, and charge rate exceeding. The abnormality is detected by the control system reading the measured values for monitoring the state of the battery and exceeding the abnormality determination threshold. When an abnormality is detected, the storage battery control system or the vehicle control system takes measures. The main measures are to issue a warning, impose restrictions on battery use, disconnect the battery, etc. Therefore, when measures are taken, it may lead to a decrease in vehicle performance or an operation stop. When the control system detects an abnormality, it reports an abnormality flag corresponding to the occurred abnormality, notifies the cab of what kind of abnormality has occurred, and records the abnormality flag together with various data of the actual vehicle in an internal or external recording medium of the vehicle.

[0004] As an example of a system for detecting abnormalities in such railway vehicles, Patent Document 1 discloses a railway vehicle energy storage system and a railway vehicle energy storage element monitoring system comprising an energy storage element, a battery management device for acquiring energy storage element information regarding the state of the energy storage element, and a communication device for transmitting the energy storage element information acquired by the battery management device to an external device. Patent Document 2 also discloses a method of entering into various service contracts with a railway company for all or part of the devices and equipment of an electric vehicle 200, constantly monitoring the operating status of the devices and equipment on board the contracted electric vehicle 200, or recording various data using a transmitting and receiving device installed on board the vehicle, or providing contracted information or services to a designated location on the ground of the railway company when a contracted event occurs. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2021-019479 [Patent Document 2] Japanese Patent Publication No. 2002-329020 [Overview of the project] [Problems that the invention aims to solve]

[0006] As a result of diligently studying countermeasures for malfunctions in railway vehicles equipped with battery systems, the inventors of this invention have obtained the following findings. Even with the same abnormality flag, the effective countermeasures will differ depending on the cause. For example, suppose an abnormality flag is triggered indicating that the battery has overheated (a phenomenon where the temperature exceeds the specified range). There are various possible causes for overheating, such as high ambient temperature, heavy battery load, battery degradation leading to increased electrical resistance, cooling system malfunction, or a faulty temperature sensor, and the effective countermeasures will differ depending on the cause.

[0007] On the other hand, when an abnormal flag occurs, it is not easy to identify the cause. Typically, the system only provides information about the type of abnormal flag and the date and time of its occurrence. When investigating the cause, someone with sufficient knowledge of the relevant railway vehicle and battery system must carefully analyze the data from the time of the abnormality and its surroundings to identify the cause. In this regard, Patent Document 2 shows an example in an electric vehicle where, when a certain fault flag occurs, the system retrieves related data and fault know-how corresponding to the fault flag, allowing even non-technical personnel to identify the fault location, provide instructions for stopping the vehicle in the driver's cab, and notify the driver of the part to be replaced and arrange for the parts to be ordered. However, Patent Document 2 can only identify the fault location; while it can replace the faulty part, it cannot provide the cause of the abnormality, nor does it propose a method for continued use without causing further problems.

[0008] Therefore, the present invention aims to identify not only the location of the malfunction but also the cause of the malfunction in an electric vehicle equipped with a drive system using a battery storage device, and to easily obtain countermeasures for the malfunction, including changes to the operation method of the electric vehicle. [Means for solving the problem]

[0009] To solve the above problems, a typical abnormality countermeasure system of the present invention is an abnormality countermeasure system used in an electric vehicle equipped with a drive system using a battery device, wherein the abnormality countermeasure system comprises a cause determination unit and a countermeasure proposal unit, the cause determination unit monitors the status of the electric vehicle and the battery device, and when an abnormality flag is output from at least one of the electric vehicle and the battery device, it analyzes the status of at least one of the electric vehicle and the battery device and identifies the cause of the abnormality indicated by the abnormality flag, the countermeasure proposal unit notifies the electric vehicle manager of the cause analysis result of the abnormality flag and the countermeasure proposal, the countermeasure proposal includes categories such as changing the operation method of the electric vehicle, changing the control parameters of the battery device or the drive system, repairing faulty software or faulty equipment of the battery device or the drive system, and replacing a deteriorated battery. [Effects of the Invention]

[0010] According to the present invention, when an electric vehicle equipped with a drive system using a battery device malfunctions, it is possible to identify not only the location of the malfunction but also the cause of the malfunction, and to obtain countermeasures, including changes to the operation method of the electric vehicle, even by a person who does not have sufficient knowledge of both the drive system using a battery device and the electric vehicle equipped with the drive system, thereby enabling early recovery. Issues, structures, and effects other than those mentioned above will be clarified by the following explanation of the implementation methods. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 shows the configuration of a drive system for a railway vehicle equipped with a battery to which the abnormality countermeasure system according to Embodiment 1 of the present invention is applied. [Figure 2] Figure 2 shows the system configuration of the drive system of a hybrid diesel railcar. [Figure 3] Figure 3 is a basic operation block diagram of the abnormality countermeasure system according to Embodiment 1 of the present invention. [Figure 4] Figure 4 shows the abnormality cause weighting table when the abnormality flag indicates battery overtemperature. [Figure 5] Figure 5 is an operational block diagram of the cause determination unit that calculates cause weights by comparing historical data with past data according to Embodiment 1 of the present invention. [Figure 6] Figure 6 illustrates the calculations performed by the cause weight calculation unit included in the cause determination unit. [Figure 7] Figure 7 is an operational block diagram of the cause determination unit when the digital twin according to Embodiment 1 of the present invention is applied. [Figure 8] Figure 8 is an operational block diagram of the model verification unit according to Embodiment 1 of the present invention. [Figure 9] Figure 9 is an operational block diagram of the sensitivity calculation unit according to Embodiment 1 of the present invention. [Figure 10] Figure 10 shows an example of an operational block diagram of a battery-powered digital twin according to Embodiment 1 of the present invention. [Figure 11] FIG. 11 is a diagram showing an example of a sensitivity map when the abnormality flag indicates battery over temperature. [Figure 12] FIG. 12 is an operation block diagram of a countermeasure proposal unit according to Embodiment 1 of the present invention. [Figure 13] FIG. 13 is a diagram showing an effective countermeasure table that shows countermeasures corresponding to cause classification according to Embodiment 1 of the present invention in association with each other. [Figure 14] FIG. 14 is a diagram showing an example of a countermeasure effect map. [Figure 15] FIG. 15 is a diagram showing an example when countermeasure information is displayed. [Figure 16] FIG. 16 is an operation block diagram of an abnormality countermeasure system having an error determination unit 33 according to Embodiment 2 of the present invention. [Figure 17] FIG. 17 is an operation block diagram of an abnormality countermeasure system having a control constant change unit according to Embodiment 3 of the present invention. [Figure 18] FIG. 18 is an operation block diagram of an abnormality countermeasure system having an abnormality omen diagnosis unit according to Embodiment 4 of the present invention. [Figure 19] FIG. 19 is an operation block diagram of an abnormality countermeasure system having an emergency measure proposal unit according to Embodiment 5 of the present invention. [Figure 20] FIG. 20 is an operation block diagram of an abnormality countermeasure system for transmitting to a manufacturer according to Embodiment 6 of the present invention. [Figure 21] FIG. 21 is an operation block diagram of an abnormality countermeasure system having a propagation evaluation unit according to Embodiment 6 of the present invention. [Figure 22] FIG. 22 is a diagram showing the configuration of a drive system for a railway vehicle equipped with a storage battery to which the abnormality countermeasure system according to Embodiment 8 of the present invention is applied. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following describes an embodiment for implementing the abnormality countermeasure proposal system according to the present invention, based on the figures. Note that the present invention is not limited to this embodiment. Furthermore, in the drawings, identical parts are denoted by the same reference numerals. The embodiments described below use an abnormality countermeasure suggestion system mounted on a railway vehicle as an example, but the present invention is not limited to this. The present invention can also be applied to, for example, a general stationary battery storage system. Furthermore, the embodiments described below will be explained using lithium-ion batteries as an example, but they can be similarly applied to other energy storage elements such as lead-acid batteries, nickel-metal hydride batteries, or capacitors.

[0013] Furthermore, in this disclosure, "target parameter" means the direct monitoring parameter corresponding to the abnormal flag. "Related parameter" means a parameter that is different from the target parameter but is related to the abnormal flag. Furthermore, "digital twin" refers to a model that replicates the vehicle and its battery.

[0014] [Example 1] (Electric vehicles to which mobility support systems are applied) Figure 1 shows the configuration of a drive system 1A for a railway vehicle equipped with a battery to which the abnormality countermeasure system according to Embodiment 1 of the present invention is applied. In the figure, solid lines indicate power transmission paths, double lines indicate torque transmission paths, and dotted lines indicate information transmission paths such as control signals and sensor values. In electrified sections, the drive system 1A is driven by the power of the overhead lines and charges the battery, and in non-electrified sections, it uses the power of the battery. First, the configuration of the drive system 1A for the railway vehicle will be explained. The information includes information showing measured values ​​and control signals. The arrows shown at both ends of the dotted lines indicate the direction in which this information is transmitted and received. In addition, at least one of the components connected by dotted lines is equipped with a sensor, and the sensor detects numerical values ​​of the state of the component. Furthermore, the solid lines are drawn between the overhead wire 14 and pantograph 2, between pantograph 2 and converter 5, between converter 5 and inverter 6 for the motor, between converter 5 and inverter 10 for the auxiliary equipment, between converter 5 and battery unit 20, between inverter 6 for the motor and motor 7, between inverter 6 for the motor and inverter 10 for the auxiliary equipment, between inverter 6 and battery unit 20, between inverter 10 for the auxiliary equipment and battery unit 20, and between inverter 10 for the auxiliary equipment and auxiliary equipment 11. The double lines are drawn between motor 7 and reduction gear 8, and between reduction gear 8 and wheelset 9. The dotted lines indicate the following connections: between the vehicle control device 13 and the pantograph 2, between the vehicle control device 13 and the converter 5, between the vehicle control device 13 and the inverter 6 for the motor, between the vehicle control device 13 and the motor 7, between the vehicle control device 13 and the wheelset 9, between the vehicle control device 13 and the inverter 10 for the auxiliary equipment, between the vehicle control device 13 and the auxiliary equipment 11, between the vehicle control device 13 and the driver's cab 12, between the vehicle control device 13 and the battery unit 20, between the vehicle control device 13 and the malfunction countermeasure system 30, between the driver's cab 12 and the malfunction countermeasure system 30, and between the malfunction countermeasure system 30 and the railway operator's control center 40. The connection between the malfunction countermeasure system 30 and the railway operator's control center 40 is preferably wireless communication.

[0015] The railway vehicle drive system 1A includes a pantograph 2 for connecting the drive system 1A to the overhead line 14, a converter 5 for converting overhead line power to DC power, an inverter 6 for an electric motor for converting DC power to AC power, an electric motor 7 that outputs torque for driving the railway vehicle, a reduction gear 8 that reduces the output of the electric motor 7 and transmits it to the wheelset 9, an inverter 10 for auxiliary equipment, an auxiliary equipment 11 used for the vehicle's lighting and air conditioning, a battery device 20, a driver's cab 12 equipped with a display that generates driving commands in response to the driver's notch operation, a vehicle control device 13 that generates control commands for the converter 5, the inverter 6 for the electric motor, and the inverter 10 for auxiliary equipment based on the driving commands transmitted from the driver's cab 12 and the status of the battery device 20, and an abnormality countermeasure system 30 that analyzes data from at least one of the battery device 20 and the vehicle control device 13 and transmits proposed countermeasures to the driver's cab 12 and the operation control center 40 managed by the railway operator.

[0016] The pantograph 2 is an electrical switch that moves up and down. When the pantograph 2 is raised and in contact with the overhead wire 14, the DC or AC power supplied by the overhead wire 14 is supplied to the converter 5 via the pantograph 2. When the pantograph 2 is in contact with the overhead wire 14, the battery-powered train runs on the power from the overhead wire 14 and charges its battery, and when the pantograph 2 is not in contact with the overhead wire 14, it uses the power from the battery.

[0017] Converter 5 takes the DC or AC power output from pantograph 2 as input, converts it into DC power corresponding to the commanded amount of energy, and outputs it.

[0018] The inverter 6 for the electric motor converts the DC power supplied via the converter 5 into three-phase AC power. The electric motor 7 takes the three-phase AC power output by the inverter 6 as input, converts it into shaft torque, and outputs it. The reduction gear 8 reduces the rotational speed of the electric motor 7 using a combination of gears with different numbers of teeth, and drives the wheelset 9 with the amplified shaft torque to accelerate and decelerate the vehicle. A speed generator (not shown) for measuring the vehicle speed is also attached to the wheelset 9.

[0019] The auxiliary inverter 10 takes the DC power between the converter 5 and the motor inverter 6 as input, converts it to three-phase AC power, and outputs it. The auxiliary equipment 11 consists of service equipment such as vehicle lighting and air conditioning systems, and operates on power supplied by the auxiliary inverter 10.

[0020] The driver's cab 12 is equipped with a display that shows the time, vehicle speed, battery information, etc., and an input device for the driver to input driving commands, etc., to the vehicle control device 13.

[0021] The battery storage unit 20 is a device that stores energy to drive the vehicle. It is charged with DC power output from the converter 5 and discharged to the inverter 6 for the electric motor and the inverter 10 for the auxiliary equipment. When the vehicle is using regenerative braking, it is also charged with DC power output from the inverter 6 for the electric motor. The battery storage unit 20 is equipped with a battery control device 21. The battery control device 21 measures the state of the battery contained in the battery storage unit 20, calculates the charge rate and allowable current (current that can be safely supplied), and communicates with the vehicle control device 13 to notify it of the state of the battery storage unit 20.

[0022] The vehicle control device 13 notifies and controls each component included in the drive system 1A. For example, the vehicle control device 13 outputs control signals to the converter 5, the inverter 6 for the electric motor, and the inverter 10 for the auxiliary equipment based on the operation command, the status of the battery device 20, the status of the pantograph 2, etc.

[0023] The abnormality countermeasure system 30 communicates with the battery unit 20 and the vehicle control unit 13. When the battery unit 20 or the vehicle control unit 13 issues an abnormality flag, the abnormality countermeasure system 30 analyzes the status of the battery unit 20 or the vehicle control unit 13 and sends a proposal for abnormality countermeasures to the driver's cab 12 or the railway operator's control center 40 outside the vehicle. The abnormality countermeasure system 30 does not necessarily have to be independent of the drive system 1A; it may be included in the vehicle control unit 13 or the battery control unit 21, or it may be installed in a data center at a separate location and receive data from the drive system 1A using wireless devices or the like and process the information. Furthermore, the abnormality countermeasure system may collect information directly without going through the vehicle control unit 13. For example, the abnormality countermeasure system 30 may be provided with a transmission path for information to components included in the drive system 1A. The abnormality countermeasure system 30 may also be equipped with sensors such as GPS and ammeters, and the status may be inferred from the measurement results. Furthermore, the abnormality countermeasure system 30 may be equipped with means of communication with the outside of the vehicle and may collect information from various facilities such as control centers located outside the vehicle.

[0024] The abnormality countermeasure system 30 of Example 1 is used in an electric vehicle equipped with a drive system using a battery storage device 20. The abnormality countermeasure system 30 is not limited to the form of a railway vehicle, as long as it includes a battery storage device. Another form is, for example, the drive system of a hybrid diesel railcar. Figure 2 is a diagram showing the system configuration of the hybrid diesel railcar drive system 1B. A hybrid diesel railcar is a railway vehicle that does not use power from overhead lines, but runs on power from an engine and power from a battery. In difference from Figure 1, the solid line showing the power transmission path is drawn between the engine 3 and the generator 4. Also, the dotted line showing the information transmission path is drawn between the engine 3 and the vehicle control device 13 and between the generator 4 and the vehicle control device 13. The engine 3 does not operate continuously, but is started, for example, when there is a continuous acceleration operation command input or when the battery charge level drops. In the hybrid diesel railcar drive system 1B, the engine 3 and generator 4 are used instead of overhead lines 14 and pantograph 2. The engine 3 outputs shaft torque according to the engine speed command value from the vehicle control device 13. Generator 4 takes the shaft torque of engine 3 as input and converts it into three-phase AC power for output. Converter 5 takes the three-phase AC power output from generator 4 as input and converts it into DC power corresponding to the commanded amount of energy for output. The subsequent system configuration is the same as that of battery-powered train drive system 1A.

[0025] In this embodiment, the case of a battery-powered train will be explained below, but this disclosure can also be applied to hybrid diesel railcars and other electric vehicles equipped with batteries.

[0026] (Countermeasure proposal system) Figure 3 is a basic operation block diagram of the abnormality countermeasure system 30 according to Embodiment 1 of the present invention. The abnormality countermeasure system 30 includes a cause determination unit 31 and a countermeasure proposal unit 32.

[0027] The cause determination unit 31 monitors the status of the electric vehicle and the battery storage device 20. If an abnormality flag is output from at least one of the electric vehicle and the battery storage device 20, it analyzes the status of at least one of the electric vehicle and the battery storage device 20 and identifies the cause of the abnormality indicated by the abnormality flag Af. Based on the inputs of the abnormality flag Af and battery data Db output from the battery control device 21 and the vehicle control device 13, and in Embodiment 1, the inputs of vehicle data Dv and weather data Dw, the cause determination unit 31 generates a cause weight table Wt and outputs it to the countermeasure proposal unit 32. In the following description, the abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw are used as inputs, but when an abnormality related to the battery occurs, the abnormality flag Af and battery data Db are mainly the focus. The cause determination unit 31 monitors the status of the vehicle control device 13 and the battery storage device 20 based on the abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw. Note that the abnormality flag Af can also be calculated from the battery data Db.

[0028] Here, the abnormality flag Af is a flag indicating an abnormality issued by the battery control device 21 or the vehicle control device 13 of the battery storage device 20. This embodiment deals with abnormalities related to the battery, and assumes abnormalities such as over-temperature, over-voltage, and over-charge rate of the battery detected by the battery control device 21, as well as communication abnormalities with the battery storage device 20. However, abnormalities related to vehicle performance detected by the vehicle control device 13 may also be included as abnormalities targeted by the abnormality flag Af. For example, an abnormality such as low vehicle acceleration ability may be caused by low battery voltage or low battery allowable current, and may be related to a battery abnormality.

[0029] Battery data Db includes measured battery sensor values ​​and calculated battery status values, which are actual values ​​of the battery sensor included in the storage battery device 20 transmitted by the battery control device 21, as well as status data of internal equipment. Measured battery sensor values ​​include battery temperature, battery current, and battery voltage. Calculated battery status values ​​include the battery charge level, battery allowable current, and battery degradation level. Status data of internal equipment includes data on the operating status and failure status of cooling devices, contactors, and other components contained within the battery control device 21 and the storage battery device 20.

[0030] Vehicle data Dv includes driving status signals of the drive system 1A transmitted by the vehicle control device 13, measured sensor values, and calculated state values. Driving status signals include notch signals determined by input from the driver's cab 12 or automatic calculation by the vehicle control device 13, brake signals, pantograph 2 raising / lowering signals, signals indicating the operating status of auxiliary equipment 11, and modulation rates of converter 5, motor inverter 6, auxiliary equipment inverter 10, etc. Measured sensor values ​​include voltage and current values ​​of converter 5, motor inverter 6, and auxiliary equipment inverter 10, as well as vehicle speed measured by speedometers, etc., and vehicle position measured by track circuits, etc. Calculated state values ​​include occupancy rates, etc.

[0031] The weather data Dw includes data such as temperature, solar radiation, rainfall, and wind speed. This data may be measured on the vehicle by the vehicle control device 13, or it may be obtained via the train control center 40 from data measured at a nearby location outside the vehicle.

[0032] (Cause weight table) The cause weight table Wt shows the causes associated with a given abnormal flag and their weights. The causes are subdivided into cause items, and then assigned cause categories and weights. Figure 4 shows the cause weight table Wt when the abnormal flag indicates battery overtemperature. The cause determination unit 31 subdivides and presents the causes of abnormal flag Af using fault tree analysis (FTA). Column names "Cause 1" to "Cause 5" correspond to the fault tree analysis for battery overtemperature. Battery overtemperature occurs primarily due to high heat generation or difficulty in cooling, as shown in Cause 1. Furthermore, Cause 1, "high heat generation," is caused by high resistance or high battery load, as shown in Cause 2. By subdividing and tracing the causes in this way, it is possible to arrive at causes that have materialized into events that can actually be dealt with, such as battery degradation as shown in Cause 3, or rapid acceleration and deceleration as shown in Cause 5. The column name "Cause Category" indicates whether the end cause of the FTA falls into one of the categories.

[0033] In this case, if the abnormality is due to battery overtemperature, the causes can be categorized into (1) sudden events, (2) abnormal environment, (3) harsh vehicle operation methods, (4) battery degradation, (5) inappropriate control parameters, (6) control program malfunction, and (7) hardware failure. (1) An unexpected event refers to a transient event, including, for example, a vehicle stopping due to an accident involving one's own vehicle or another vehicle, or exceeding capacity due to a large-scale event. (2) An abnormal environment refers to conditions such as abnormal temperatures or snowfall. (3) A harsh operating method for a vehicle indicates that the vehicle is subjected to harsh operating conditions under normal circumstances, such as when there is no margin in the timetable, or when the normal passenger load and auxiliary equipment power consumption exceed the expected values. (4) Battery degradation refers to a state in which the degradation of a battery progresses, resulting in a decrease in capacity and an increase in resistance. (5) Inappropriate control parameters refer to a state in which the parameters used to operate the vehicle or battery are inappropriate, resulting in unsatisfactory performance or excessive load being applied. (6) A malfunction in the control program refers to a state in which a function is not working due to a problem in the control program. In this case, the function will work if the program is corrected. (7) Hardware failure refers to a state in which some equipment is malfunctioning and its function is not working. In this case, the function will work again if the hardware is repaired or replaced.

[0034] As explained above, the cause determination unit 31 classifies the cause of the abnormal flag Af into one of the following categories: (1) sudden event, (2) abnormal environment, (3) harsh vehicle operation method, (4) battery degradation, (5) inappropriate control parameters, (6) control program malfunction, or (7) hardware failure. However, the classification is not necessarily limited to these categories. The purpose of the classification is to determine the countermeasures to be taken. The cause of the abnormality is identified by analyzing the state of the vehicle or battery using the data described above. The classification of countermeasures proposed for each cause category will be described later.

[0035] The countermeasure proposal unit 32 notifies the electric vehicle manager of the cause analysis results and countermeasure proposals for the abnormal flag Af. In other words, the countermeasure proposal unit 32 takes the cause weight table Wt output from the cause determination unit 31 as input and outputs countermeasure information Ci to the driver's cab 12 and the operation control center 40. The countermeasure information Ci includes the cause analysis results, countermeasure proposals, and the effects of the countermeasures. The countermeasure information Ci is notified to the electric vehicle occupants in the driver's cab 12, and the countermeasure information is notified to the operator of the electric vehicle in the operation control center 40.

[0036] The cause analysis results here are the results of analyzing the anomalies indicated by the anomaly flags, and serve as the basis for calculating the proposed countermeasures and their effects. The proposed countermeasures propose solutions to the causes of the anomaly flags, or in other words, countermeasures for each cause in the cause weighting table Wt. For example, if the cause is battery degradation, the countermeasure is battery replacement; if the cause is sudden acceleration and deceleration, the countermeasure is to change the parameters related to acceleration settings and their values ​​after the change. The effect of the countermeasures shows the expected effect of how the parameters corresponding to the anomaly flags will change if the countermeasures proposed in the proposed countermeasures are implemented. Furthermore, if the countermeasures affect not only the indicators of the anomaly itself but also the condition of the vehicle or battery, the impact on the countermeasures is also shown. For example, if the vehicle's acceleration is slowed, it may affect the time it takes to reach stations.

[0037] (Calculation of weights for the causes of anomalies) There are several methods by which the cause determination unit 31 calculates the cause weights in the cause weight table Wt, and two of these methods are introduced in Example 1. The first method is a method of calculation by comparing with past data. Figure 5 is an operational block diagram of the cause determination unit 31 that calculates cause weights by comparing with past data according to Example 1 of the present invention. In the first method, the cause determination unit 31 has a related parameter extraction unit 311, a cause weight calculation unit 312, and a storage area 313. The storage area 313 records past abnormality flags, battery data, vehicle data, and weather data. If only unprocessed data is recorded, the amount of data will be enormous, so it is also possible to apply statistical processing such as maximum value, minimum value, mean value, and mean squares according to the nature of the data.

[0038] (First method for weight calculation) The related parameter extraction unit 311 has the function of extracting related parameters corresponding to the abnormal flag, and takes the abnormal flag, battery data, vehicle data, and weather data as input, and selects and outputs the target parameter Tpe and related parameter Rpe when an abnormality occurs. Here, the target parameter is the direct monitoring parameter corresponding to the abnormal flag. Related parameters are parameters other than the target parameter that are related to the abnormal flag. For example, if the abnormality indicated by the abnormal flag is battery overtemperature, the target parameter will be the maximum battery temperature, and related parameters will be current, ambient temperature, and signals indicating the operating status of the cooling system. Related parameters are further subdivided according to FTA as shown in Figure 4. For example, as parameters related to current, parameters such as notch signal, inverter current, converter current, occupancy rate, auxiliary equipment consumption current, air conditioner consumption current, and occupancy rate are further extracted and used as candidates for related parameters.

[0039] The cause weight calculation unit 312 takes the target parameter Tpe and the related parameter Rpe obtained from the related parameter extraction unit 311 as input, along with the past history of the target parameter and related parameter, namely the abnormal target parameter Tph and the abnormal related parameter Rph, read from the storage area 313, and calculates and outputs a cause weight table Wt. One possible calculation method is to check the time evolution of the value of the related parameter Rph in the past history relative to the target parameter Tph in the past history, add weights to those that evolve over time as candidate causes of the abnormality, and take the ratio of the value of the related parameter at the time of the abnormality to the value of the related parameter in the state before the abnormality occurred, and use those with a large ratio as weights.

[0040] The cause determination unit 31 checks the history of parameters related to the abnormal flag and assigns weights to parameters that have changed significantly in the history. This will be explained with reference to Figure 6. Figure 6 is a diagram illustrating the calculations performed by the cause weight calculation unit 312 included in the cause determination unit 31. "Date" is the date on which the parameters were extracted. "Maximum cell temperature" is the maximum temperature of the battery cells in the battery system. "Current RMS" is the RMS (root mean square value) of the current flowing through the battery cells, "Operating time" is the operating time of the train, and "Maximum temperature" is the temperature during the operating time. Here, parameters are obtained for each date. If the abnormality is due to battery overtemperature, the relevant parameter Tp is "maximum cell temperature," and the related parameters Rp are "current RMS," "operating time," and "maximum temperature." The data shows a case where battery overtemperature occurred on August 15, 2022. The relevant parameter Tpe during the abnormal situation is "Maximum Cell Temperature" at 55°C, and the related parameters Rpe are "Current RMS" at 70, "Operating Time" at 15h, and "Maximum Temperature" at 38°C. Also shown are the related parameters Rph from the historical data for August 13 and August 14, 2022.

[0041] The cause determination unit checks the history of the parameters related to the abnormal flag, and uses the ratio of the past parameter value to the latest parameter value in the history as the weight. Here, the time evolution of the past history values ​​of the related parameters is checked. The time evolution of "current RMS" is 51 (August 13, 2022), 50 (August 14, 2022), and 70 (August 15, 2022). The time evolution of "operating time" is 15h (August 13, 2022), 15h (August 14, 2022), and 15h (August 15, 2022). "Operating time" is a common value. The time evolution of "maximum temperature" is 35℃ (August 13, 2022), 36℃ (August 14, 2022), and 38℃ (August 15, 2022). Considering the time evolution from the past history to the time of the abnormality, current RMS has the largest change and is considered the first candidate for the cause of the abnormality. Maximum temperature, which has the next largest change, is the second candidate. Since there is no change in operating time, it is excluded from the list of possible causes of the anomaly. Furthermore, if we take the ratio of the values ​​of the relevant parameters at the time of the anomaly to the values ​​of the relevant parameters in the state before the anomaly occurred in the past, the current RMS is 70 / 50 = 1.4, the operating time is 15h / 15h = 1, and the maximum temperature is 38 / 36 ≈ 1.05. From the past history, the changes during the anomaly are current RMS:operating time:maximum temperature = 0.4:0:0.05 = 0.8:0:0.2. This is used as the weight for the candidate causes of the anomaly. Alternatively, the maximum temperature can be taken as the reciprocal of the temperature difference from the anomaly alarm temperature of 55℃, in which case (1 / (55-38)) / (1 / (55-36)) ≈ 1.12. In this case, from the past history, the changes during the anomaly are current RMS:operating time:maximum temperature = 0.4:0:0.12 = 0.77:0:0.23 (weight with the sum equal to 1). In this way, these can be used as weights for candidate causes of the anomaly.

[0042] (Second method for weight calculation) A second method involves using reproduction models (digital twins) of batteries and vehicles. Figure 7 is an operational block diagram of the cause determination unit 31 when the digital twin according to Embodiment 1 of the present invention is applied. The cause determination unit 31 includes a model verification unit 314, a sensitivity calculation unit 315, and a cause weight calculation unit 312. The cause determination unit 31 has a digital twin of the vehicle and a digital twin of the battery as models.

[0043] As described later, the model verification unit 314 and the sensitivity calculation unit 315 have a digital twin and perform processing using the calculated values ​​which are the output of the digital twin. In order to identify the cause of an anomaly using the digital twin, it is first necessary to check whether the digital twin reproduces the measured values. Therefore, the model verification unit 314 has a function to calculate whether the calculated values ​​of the state values ​​match the measured values ​​of the state values ​​when the state values ​​(calculated values) of the battery and vehicle reproduced by the digital twin are recalculated. In other words, the model verification unit 314 calculates the state values ​​using the digital twin, taking the anomaly flag Af, battery data Db, vehicle data Dv, and weather data Dw as input, compares them with the measured values ​​of the state values, and outputs the comparison result Cr. If it is confirmed from the comparison result Cr that the accuracy of the digital twin is not a problem, it becomes possible to use the sensitivity for each parameter calculated by the sensitivity calculation unit 315 as a weight. On the other hand, if there is a problem with the accuracy of the digital twin, the cause of the anomaly will be identified from the discrepancy between the calculated values ​​of the digital twin and the measured values ​​and weights will be assigned.

[0044] More specifically, the model verification unit 314 has a digital twin of the battery and vehicle and recalculates the target parameters and related parameters of the anomaly flag. The comparison result Cr is calculated for multiple target parameters and related parameters of the anomaly flag by comparing whether the measured value and the calculated value match at least the parameter being calculated and the parameter used in the calculation. What this process reveals is whether a certain parameter is behaving as expected in the model, which is useful, for example, when detecting equipment failure. For example, battery temperature can basically be calculated from ambient temperature, current value, battery degradation level, and cooling system operating status signal. If the measured or calculated battery temperature is greater than the calculated value, a discrepancy occurs between the digital twin and the actual system. This basically arises from three causes: sensor anomaly, battery state calculation error, and equipment failure. Sensor anomaly means that noise is superimposed on the measured values ​​of temperature or current, resulting in incorrect values. Battery state calculation error means that parameters that cannot be directly detected by sensors and are calculated indirectly, such as charge rate and degradation level, are incorrect. Equipment failure means that there are no problems with the operation signals and response signals, but there is a problem with the actual operation. In an actual battery storage device 20, if the measured temperature differs from the calculated temperature, it is rare for there to be a problem with the temperature sensor or current sensor. Therefore, the investigation can proceed by suspecting a calculation error in the battery degradation rate and a failure of the cooling device. In addition, the three causes mentioned above can also be distinguished from the behavior of their respective values. For example, if the temperature sensor or current sensor repeatedly shows the minimum value calculated by the model and the actual value alternately, a poor contact can be suspected. Details of the functional blocks, etc., will be described later. In this way, when the calculation result of the digital twin and the measured value do not match, the cause determination unit determines whether the problem is a sensor malfunction, a battery state calculation error, or equipment failure. For example, when the measured value is larger than the calculated temperature result of the battery digital twin 3143, the cause determination unit 31 determines Determine if the fan is faulty.

[0045] The sensitivity calculation unit 315 has the function of calculating the strength of the influence (sensitivity) that changes in each related parameter have on the target parameter of the abnormality flag. The sensitivity calculation unit 315 takes the abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw as input and outputs a sensitivity map Sm of the related parameters. By using a digital twin, the target parameter can be calculated while changing the related parameters, and the sensitivity can be calculated by taking the ratio of the amount of change in the related parameters to the amount of change in the target parameter.

[0046] The cause weight calculation unit 312 has the function of calculating cause weights and outputting a cause weight table Wt. It calculates cause weights using the comparison result Cr and the sensitivity map Sm of related parameters as inputs. If the comparison result Cr indicates that the measured value and the calculated value do not match, and sensor errors, battery state calculation errors, or equipment failures are suspected, then weights are concentrated on equipment failures or problems with the state calculation program. If the comparison result Cr indicates that the measured value and the calculated value match, the weights are determined from the sensitivity map Sm of related parameters.

[0047] Figure 8 is an operational block diagram of the model verification unit 314 according to Embodiment 1 of the present invention. The model verification unit 314 includes a vehicle digital twin 3142, a battery digital twin 3143, and a determination unit 3141.

[0048] The Vehicle Digital Twin 3142 has the function of recalculating vehicle data, which is a state value calculated from a vehicle model, based on measured data, which is the state value of the vehicle measured in real time. It takes abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw as inputs and outputs the calculated abnormality flag Afc1 and the calculated vehicle data Dvc. For example, it recalculates inverter current, converter current, power consumption, and fuel consumption from vehicle speed, notch information, and brake signals.

[0049] The Battery Digital Twin 3143 has the function of recalculating battery data, which is a state value calculated from a battery model, based on measured data, which is the state value of the battery measured in real time. It takes abnormality flag Af, battery data Db, vehicle data Dv, and weather data as inputs, and outputs the calculated abnormality flag Afc2 and the calculated battery data Dbc. For example, it calculates the battery temperature from signals indicating battery current, temperature, degradation level, and the operating status of the cooling system. In addition, the battery data Dbc may also include the battery degradation rate for the input usage conditions.

[0050] In the vehicle digital twin 3142 and the battery digital twin 3143, recalculating all parameters would result in an enormous amount of computation. Therefore, as described later, a configuration may be adopted that includes a calculation target designation unit 31431 that limits the calculation area in response to an abnormal flag.

[0051] The determination unit 3141 has a function to determine the agreement between measured and calculated values, and takes the abnormality flag AFc, vehicle data Dvc, and battery data Dbc as inputs, and outputs the comparison result Cr. Agreement between measured and calculated values ​​is determined, for example, by whether or not they deviate from the accuracy range predetermined for each calculation method. The accuracy can be set by comparing the calculated and measured values ​​of the digital twin at a time when all equipment is operating normally. Note that the abnormality flag Afc1 output from the vehicle digital twin 3142 and the abnormality flag Afc2 output from the battery digital twin 3143 do not necessarily have to be the same. In Embodiment 1, since an abnormality occurs when an abnormality occurs in the battery, the determination unit 3141 can make a determination based on the abnormality flag AFc2 output from the battery digital twin 3143, but this disclosure is not limited to this. The determination unit 3141 may also determine an abnormality in the entire vehicle based on both abnormality flags.

[0052] (Method for calculating sensitivity) Figure 9 is an operational block diagram of the sensitivity calculation unit 315 according to Embodiment 1 of the present invention. The cause determination unit 31 calculates the sensitivity of the abnormal flag Af to the target parameter by slightly changing the parameter related to the abnormal flag Af, and calculates the weight of the cause of the abnormal flag Af. This will be explained with reference to Figure 9. The sensitivity calculation unit 315 includes a minute change generation unit 3152, a vehicle digital twin 3142, a battery digital twin 3143, and a sensitivity map generation unit 3151. The vehicle digital twin 3142 and the battery digital twin 3143 are the same as those provided in the model verification unit 314, but as will be described later, they also have the functions of the sensitivity calculation unit 315. Furthermore, the vehicle digital twin 3142 and the battery digital twin 3143 may be provided one each in the abnormality countermeasure system 30 and have a common configuration for the model verification unit 314 and the sensitivity calculation unit 315, or they may be provided as separate configurations for the model verification unit 314 and the sensitivity calculation unit 315, respectively.

[0053] The minute change generation unit 3152 has the function of calculating minute change data to be input to the vehicle digital twin 3142 and the battery digital twin 3143. It takes the abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw as inputs and outputs minute change battery data ΔDb, minute change vehicle data ΔDv, and minute change weather data ΔDw, which are data with minute changes. Normally, there are countless ways to choose the parameters to be changed and the amount of minute change. For example, if the parameters to be changed and the amount of change or ratio are predetermined in relation to the abnormality flag Af, the computational load can be reduced.

[0054] The vehicle digital twin 3142 has a function to recalculate vehicle data based on minute change data. It takes an anomaly flag Af, minute change battery data ΔDb, minute change vehicle data ΔDv, and minute change weather data ΔDw as inputs, and outputs the target parameter Tpc1 and minute change battery data ΔDbc, which are calculated values ​​corresponding to the input data. The minute change battery data ΔDbc output here is the minute change battery data that has changed due to the influence of the minute change vehicle data ΔDv and minute change weather data ΔDw. For example, if the vehicle notch changes, the effect will also propagate to the battery current, and therefore the minute change battery data will also change.

[0055] The Battery Digital Twin 3143 has a function to recalculate battery data based on minute change data, and takes the abnormality flag Afc, minute change battery data ΔDb, minute change vehicle data ΔDv, and minute change weather data ΔDw as inputs, and outputs the target parameter Tpc2, which is a calculated value corresponding to the input data.

[0056] The sensitivity map generation unit 3151 has the function of calculating the sensitivity of related parameters to a target parameter from the calculation results of the digital twin. It takes the abnormality flag Afc, minute change battery data ΔDb, minute change vehicle data ΔDv, minute change weather data ΔDw, and target parameters Tpc1 and Tpc2 as inputs and outputs a sensitivity map Sm of the related parameters. For example, if, for a certain abnormality flag, the amount of minute change ΔXk of related parameter k corresponds to a change ΔXi in the target parameter i, then the sensitivity map Sm can be calculated as sensitivity = ΔXi / ΔXk.

[0057] (Battery-powered digital twin configuration) Figure 10 shows an example of an operational block diagram of a battery digital twin 3143 according to Embodiment 1 of the present invention. The battery digital twin 3143 includes a calculation target designation unit 31431, a temperature model 31432, a closed-circuit voltage model 31433, a charge level model 31434, and a degradation degree model 31435. The battery digital twin that calculates the battery storage device 20 includes the temperature model 31432, the closed-circuit voltage model 31433, the charge level model 31434, and the degradation degree model 31435. In other words, the battery digital twin 3143 can also calculate the battery temperature, closed-circuit voltage, and charge level.

[0058] The calculation target specification unit 31431 is a switch that limits the calculation area in response to an abnormality flag in order to reduce the computation load. It takes the abnormality flag Af as input and selects the model to be calculated. For example, if an over-temperature abnormality occurs, the temperature model 31432 and the degradation degree model 31435 are enabled. If there is sufficient computation load capacity, the closed-circuit voltage model 31433 and the charge level model 31434 may also be enabled.

[0059] The temperature model 31432 has a function to calculate the battery temperature, taking battery data Db, vehicle data Dv, and weather data Dw as inputs, and outputting the battery temperature Bt. The battery temperature Bt can be calculated, for example, by calculating the amount of heat generated from the current value and using the thermal circuit network model of the battery storage device 20.

[0060] The closed-circuit voltage model 31433 has the function of calculating the closed-circuit voltage of a battery, and takes battery data, vehicle data, and weather data as inputs and outputs the closed-circuit voltage Cv. A well-known method for calculating the closed-circuit voltage Cv from the charge level, battery temperature, current value, and battery resistance is available and can be appropriately adopted in this embodiment.

[0061] The charge rate model 31434 has a function to calculate the charge rate of a battery, taking battery data, vehicle data, and weather data as inputs, and outputting the charge rate Sc. A well-known method for calculating the charge rate Sc from the battery voltage and current value is available and can be appropriately adopted in this embodiment.

[0062] The degradation model 31435 has the function of calculating the degradation degree Dd of a battery, and takes battery data, vehicle data, and weather data as inputs to output the degradation degree. Battery degradation is known to cause both a decrease in capacity and an increase in resistance, and both of these phenomena are addressed. Methods for calculating the degradation degree Dd from voltage, current, charge level, temperature, etc. are well known and can be appropriately adopted in this embodiment.

[0063] All of these models may calculate representative values ​​such as average, maximum, and minimum values ​​for all battery cells in the battery storage device 20, or, if the computational load is acceptable, they may calculate them individually for all battery cells in the battery storage device 20. Individual calculations would allow for a more detailed analysis of the location and cause of the abnormal flag Af.

[0064] Since the battery digital twin 3143 is a model that reproduces the state of the battery storage device 20, this embodiment is not limited to a configuration that includes the four models described above, but can also include other models. Examples include a calculation model for the auxiliary power consumption of the battery storage device 20, a calculation load calculation model for the battery control device 21, and a stress calculation model for the housing of the battery storage device 20.

[0065] While numerous models can be conceivable to represent the state of the battery storage device 20, the most common abnormalities are overtemperature, overvoltage, and overcharge. Since these are detected by battery temperature, closed-circuit voltage, and charge level, it is sufficient to calculate at least these three.

[0066] Overheating occurs when the battery temperature becomes so high or low that it deviates from the operating range of the battery storage device 20. High temperatures are basically caused by high ambient temperature and high battery load, while low temperatures are caused by low ambient temperature.

[0067] Overvoltage occurs when the closed-circuit voltage of the battery becomes so high or low that it deviates from the specifications of the battery storage device 20. The closed-circuit voltage refers to the voltage between the positive and negative terminals of the battery when it is energized. Overvoltage occurs during battery charging, and undervoltage occurs during battery discharge.

[0068] Overcharging occurs when the battery's charge level becomes so high or low that it deviates from the operating range of the battery storage device 20. The charge level corresponds one-to-one with the voltage of the battery when there is no current (open-circuit voltage). Overcharging to a low charge level corresponds to a lack of energy and is called depletion of power; if this occurs in a battery-powered train, it will render the train unable to run. Overcharging to a high charge level requires an operation that exceeds the specification limit, so it basically does not occur unless there is a control error.

[0069] Furthermore, the vehicle digital twin 3142, like the battery digital twin 3143, includes a calculation model for calculating important vehicle parameters. The vehicle digital twin can appropriately select and adopt models such as one that calculates inverter current from notch and speed information.

[0070] (Example of a sensitivity map) Figure 11 shows an example of a sensitivity map Sm when the abnormal flag indicates battery overtemperature. Figure 11(a) is a sensitivity map showing the sensitivity of related parameters to the target parameter, maximum cell temperature. Related parameters include battery current RMS, maximum temperature, and operating time, with sensitivities of 0.7, 0.2, and 0.1, respectively. Figure 11(b) is a sensitivity map of the related parameter, current RMS. Related parameters include rapid charging current, air conditioner operation time, vehicle acceleration performance, and vehicle braking amount, with sensitivities of 0.1, 0.3, 0.5, and 0.1, respectively. Figure 11(c) is a sensitivity map of the related parameter, air conditioner operation. Related parameters include set temperature and maximum temperature, with sensitivities of 0.6 and 0.4, respectively. Note that the sensitivities shown here are normalized, but are not limited to this.

[0071] When referring to the sensitivity map for the target parameter, the maximum cell temperature, the most sensitive related parameter is the current RMS, as shown in Figure 11(a). Next, when referring to the sensitivity map for current RMS, the most sensitive related parameter is the air conditioner operating time, as shown in Figure 11(b). Finally, when referring to the sensitivity map for air conditioner operation, the most sensitive related parameter is the set temperature, as shown in Figure 11(c). In this way, by tracing the highly sensitive parameters in a tree-like manner, it is possible to identify parameters for which countermeasures can be taken.

[0072] (Countermeasure Proposal System - Change Scope Calculation Unit) Figure 12 is an operational block diagram of the countermeasure proposal unit 32 according to Embodiment 1 of the present invention. The countermeasure proposal unit 32 includes a changeable range calculation unit 321, a countermeasure effect calculation unit 322, and a proposal decision unit 323.

[0073] The countermeasure proposal unit 32 sets the modifiable range of parameters related to the abnormality flag Af, and sets the proposed parameter change values ​​within the said modifiable range. More specifically, the modifiable range calculation unit 321 has the function of calculating the extent to which changes can be made when changing the control method of the battery or vehicle as a countermeasure, and takes the cause weight table Wt and the past data Pd (described later) as input, and outputs the parameter modifiable range Vr. Here, even if it is likely that the control parameters of the battery storage device 20 should be changed according to the cause weight table Wt, there is a limit to the range in which they can be changed. The modifiable range calculation unit 321 specifies the modifiable range of the related parameters for which weights have been set from the cause weight table Wt. There are two reasons for specifying the modifiable range: the first is that each related parameter has its own limit on the range of change, and the second is that changing the related parameters may cause different malfunctions or deviations from specifications.

[0074] Regarding the first basis for specification, each related parameter has a defined range within which it can be individually modified. If the related parameter is related to the occurrence of an accident or is weather data Dw, the accident or extreme weather cannot be manipulated, and therefore the related parameter corresponding to the cause cannot be changed. There are limits to the adjustment range for timetables and passenger occupancy rates that have been agreed upon in advance with the railway operator. In the case of battery degradation, the improvement value of the battery degradation is limited depending on whether or not a replacement battery is used, and whether or not the replacement battery is used.

[0075] The second basis for the designation is that all changes to related parameters should be verified. The countermeasure proposal unit 32 sets the range of changes based on whether the deterioration of the target parameters of abnormal flags other than the abnormal flag that occurred is within a threshold. The countermeasure proposal unit 32 also recalculates the abnormal flags from the vehicle and battery data. To explain in more detail, it usually verifies by inputting the vector of the related parameters to be verified into the vehicle digital twin and the battery digital twin, and recalculating the target parameters corresponding to all abnormal flags. For example, for battery overtemperature or overvoltage, reducing the battery current value is an effective countermeasure, but this may reduce the vehicle's performance and make it impossible to maintain the diagram. Similarly, for battery depletion, increasing the battery charging speed may be an effective countermeasure, but this may lead to battery overtemperature. Since the conditions under which other malfunctions occur differ depending on the usage environment, it is necessary to refer to past data Pd for verification. Here, past data Pd is data that records malfunctions that have occurred in the past and the state values ​​of the battery and vehicle at the time the malfunction occurred. Furthermore, the changeable range calculation unit 321 takes into account past data stored in the storage area 313 and specifies the changeable range of the related parameters based on the specified basis. Although the storage area 313 has the same configuration as the cause determination unit 31, it is not limited to this. Separate storage areas may be provided for the cause determination unit 31 and the countermeasure proposal unit 32.

[0076] The range within which the related parameters can be changed is the overlapping range of the changeable parameters based on the two specified criteria mentioned above.

[0077] (Anomaly Countermeasure System - Countermeasure Effectiveness Calculation Unit) Generally, since weighting anomaly countermeasures is effective when multiple control parameters are changed simultaneously, the modifiable range Vr of related parameters takes the form of a multi-dimensional map where multiple related parameters are changed at the same time.

[0078] The countermeasure effect calculation unit 322 has the function of calculating the value of the target parameter and the value of the target parameter for another anomaly when the related parameters are adjusted within the changeable range Vr of the related parameters. It takes the cause weight table Wt, the changeable range Vr of the related parameters, and past data Pd as inputs and outputs a countermeasure effect map Cm. The countermeasure effect map Cm contains vectors of multiple related parameters, the value of the target parameter at that time, and the value of another target parameter. Another target parameter refers to a target parameter other than the target parameter of the anomaly flag. For example, if the anomaly flag is overtemperature and the countermeasure is to change the current, it means that the unit calculates the target parameters for other malfunctions such as charge rate and diagram compliance. The past data Pd used by the countermeasure effect calculation unit 322 is, for example, stored in association with the changes that occurred in the target parameter when countermeasures were taken on the related parameters. The changes may be statistically expressed values ​​or theoretical values.

[0079] The proposal decision unit 323 has the function of determining the content to propose to the driver's cab 12 and the operation control center 40. It takes the cause weighting table Wt and the countermeasure effect map Cm as inputs and outputs countermeasure information Ci, which includes the cause analysis results, countermeasure proposals, and countermeasure effects. Countermeasure proposals are selected based on the areas with large weights in the cause weighting table Wt. In addition to subdividing the cause of a certain abnormal flag as shown in Figure 4 and assigning a weight to each cause indicating the degree of influence, there are generally multiple effective countermeasures for a single cause, as shown in Figure 12 later. As a result, the content presented by the cause determination unit 31 may include multiple causes and countermeasures.

[0080] The results of parameter changes after countermeasures, such as changes to continuous parameters, are proposed in the countermeasure effect map Cm under the condition that they affect the target parameter of the abnormal flag and that the deterioration range of other abnormal target parameters falls within an acceptable range. The proposal decision unit 323 may be input not only performance constraints of the vehicle and battery, but also commercial constraints such as the number of replaceable parts and maintenance plans, and these may be reflected in the proposal. Information regarding the number and constraints may be stored in the storage area 313, for example, and extracted by the proposal decision unit 323 as needed. The countermeasure effect is notified as the improvement effect of the target parameter under the above conditions and the deterioration effect of other abnormal target parameters. The cause-and-effect analysis is proposed as the output of the cause weight table Wt.

[0081] (Causes and countermeasures, categorized by case) Figure 13 is a table of effective countermeasures showing the correspondence between the cause categories according to Embodiment 1 of the present invention. Just as there are multiple causes for a given abnormal flag as shown in Figure 4, there is not just one effective countermeasure for a single subdivided cause. The table of effective countermeasures in Figure 13 is a general overview of effective countermeasures for cause categories. There are seven types of cause categories, while there are five types of effective countermeasure categories. Note that this table is just an example and is not necessarily limited to this combination.

[0082] The first category of countermeasures is (A) modification of vehicle operation methods and / or control parameters under special conditions. Countermeasures in category (A) are methods of changing the vehicle operation methods and control methods only under the same special conditions as when the abnormal flag occurred. Countermeasures in category (A) are effective in cases of cause categories (1) sudden events and (2) abnormal environments. In other words, the countermeasure proposal unit 32 proposes changes to the vehicle operation methods and control parameters under special conditions when the cause is classified as either (1) sudden events or (2) abnormal environments. This is because conditions such as sudden events like accidents and abnormal environments are extreme conditions and rarely occur. Under such conditions, the requirements are different from those during normal operation. For example, the requirement may be simply to reach the nearest major station safely, and it is acceptable for acceleration performance to be much lower than under normal conditions. Specific countermeasures include, for example, the adoption of a special timetable specifically designed to reach the nearest major station, a special power usage method that supplies power only to the equipment necessary to reach the nearest major station, and control parameters suitable for these special timetables and power usage methods. Furthermore, the control parameters are not limited to those for controlling the vehicle control device 13 and the battery control device 21, but also include manually operated parameters such as the vehicle's acceleration. Changes to the vehicle operation method and control parameters under special conditions will only be made under special conditions, as applying them during normal operation would significantly restrict normal operation.

[0083] The second category of countermeasures is (B) changes to the vehicle operation method. Countermeasures in category (B) are proposed not only when an abnormal flag is generated, but also during normal times. The vehicle operation method, in other words, is the service provided by a particular vehicle as determined by the operator. Countermeasures in category (B) are effective in the following causes: (2) abnormal environment, (3) harsh vehicle operation method, and (4) battery degradation. Specific countermeasures include, for example, relaxing the timetable, relaxing the passenger load, and relaxing the power consumption of auxiliary equipment. Relaxing the timetable means reducing the daily mileage, increasing the stopping time at charging stations, and averaging the load between train sets. The fact that these are effective not only in cause category (3), but also in cause categories (2) and (4) can be explained as follows: In the case of cause category (2), if a timetable that works even in an abnormal environment is created in advance, there will be no disruption to the timetable when an abnormal environment occurs. Also, if the timetable is changed to take into account an abnormal environment that has occurred once, the disruption to the timetable will not occur, or will be minimized, even in the same abnormal environment. Regarding cause category (4), even if the malfunction is caused by battery degradation, the train can still run if the timetable is relaxed, allowing for continued operation of the train while postponing expensive battery replacement.

[0084] The third category of countermeasures is (C) modification of control parameters. Countermeasures in category (C) are proposed not only when an abnormal flag occurs, but also during normal operation. Control parameters are not limited to parameters for controlling the vehicle control device 13 and the battery control device 21, but also include manually operated parameters such as vehicle acceleration. Unlike the relaxation of vehicle operation methods in category (B), modification of control parameters does not require a change in vehicle operation methods, in other words, a change in the vehicle services provided by the operator. Countermeasures in category (C) propose changing control parameters while maintaining vehicle operation methods. Countermeasures in category (C) are effective for cause categories other than cause category (7) hardware failure, as they can improve the situation without changing vehicle operation methods for various causes. Examples of steady-state control parameters include vehicle acceleration, charging speed, target charging rate, and cooling system operation command. For cause categories (1) and (3), control parameters should be set in advance to deal with sudden occurrences or abnormal environments, or control parameters should be set to take into account sudden events or abnormal environments that have occurred once. More specifically, control parameters should be set to increase the normal charge rate to avoid running out of power, or to reduce the charging speed to avoid abnormally high temperatures.

[0085] The fourth category of countermeasures is (D) repair of faulty software and faulty equipment. Countermeasures in category (D) involve repairing faulty software that does not work due to bugs, or equipment that has failed due to hardware malfunction. Regarding faulty software, for example, software malfunctions may occur in the operation of control software for battery cooling equipment control or battery protection, resulting in unintended operation. Regarding hardware failures, for example, hardware malfunctions may occur in battery cooling equipment, contactors, auxiliary power supplies, various control boards, connectors, various sensors, etc. Since the malfunction causes an abnormality and prevents the equipment from performing as designed, and the repair itself does not cause a decrease in vehicle performance or require restrictions on vehicle operation, this countermeasure is addressed with priority. Countermeasures in category (D) are effective for cause categories (6) control program malfunctions and (7) hardware failures. In other words, the countermeasure proposal unit 32 prioritizes addressing the cause when the cause is classified as either (6) control program malfunction or 7 (7) hardware failure. Alternatively, the malfunctioning equipment may be replaced with a new or spare part before being repaired.

[0086] The fifth category of countermeasures is (E) battery replacement. Countermeasures in category (E) involve replacing the batteries on a vehicle that have deteriorated with batteries that have deteriorated less. Since battery replacement is very costly, if other countermeasures that do not require changes to the vehicle's operation method can be adopted, those countermeasures will be prioritized.

[0087] As explained above, the proposed countermeasures include any of the following categories: changes to vehicle operation methods and / or control parameters under special conditions, changes to permanent vehicle operation methods, changes to permanent control parameters, repair of faulty software and equipment, and replacement of degraded batteries. In other words, the countermeasure proposal unit 32 proposes multiple countermeasures for each individual cause of the abnormal flag Af.

[0088] (Countermeasure effectiveness map) Figure 14 shows an example of a countermeasure effect map Cm. Here, the countermeasure effect map Cm is schematically shown when two related parameters are extracted for the target parameter of battery overtemperature. For the target parameter of battery overtemperature, "Implement countermeasure A" and "Do not implement countermeasure A" indicate whether or not to adopt a countermeasure for a certain related parameter. Similarly, "Implement countermeasure B" and "Do not implement countermeasure B" indicate whether or not to adopt a countermeasure for a different related parameter. As a countermeasure effect, it is shown that the battery temperature drops by 10°C when "Implement countermeasure a" and "Do not implement countermeasure b". It is also shown that the battery temperature drops by 5°C when "Do not implement countermeasure a" and "Implement countermeasure b". In the countermeasure effect calculation unit 322, countermeasures a and b are set based on the changeable range Vr of the related parameter, and the countermeasure effect is calculated based on past data Pd.

[0089] (Countermeasure information) Figure 15 shows an example of how countermeasure information Ci is displayed. This information is output from the abnormality countermeasure system 30 and is displayed on the display unit of the driver's cab 12 or notified to the train control center 40. In the display, the information is divided into the items of "priority," "countermeasure," and "effect." The item "priority" indicates the recommended order of implementation. For example, the priority is shown from those with the greatest effect against the abnormality, based on the countermeasure effect map Cm. The item "countermeasure" shows what should be done by the crew operating the driver's cab 12 and the railway operator managing the train control center 40. The item "effect" shows the expected effect that will result from the countermeasure.

[0090] For example, for priority level 1, the suggested solution is to "change the fast charging current from the current X[A] to Y[A]." Here, the solution is to change the current value of the fast charging current. In addition, the solution information Ci includes the results of the cause analysis, the proposed solution, and the effect of the solution, and for priority level 1, it is also indicated that the cause lies in the fast charging current.

[0091] Furthermore, the effect of the priority 1 measure is stated as, "This will reduce the maximum battery temperature from X1°C to Y1°C under today's (August 15th) temperature conditions." As the temperature reduction is specifically shown, the quantitative effect of the proposed measure is communicated. This is also the case for priority 2 and 3 measures.

[0092] Furthermore, priority level 2 suggests the countermeasure "Increase stopping time by 30 seconds in the timetable." This also includes a suggestion to relax vehicle operation procedures. Priority level 3 suggests the countermeasure "Replace the batteries with new ones." This allows the crew and railway operators to understand that the cause of the battery overtemperature anomaly is the battery.

[0093] (Effects / Actions) As described above, in this embodiment, when a malfunction occurs in the vehicle's battery storage device 20, the crew and the railway operator can identify not only the location of the malfunction but also the cause of the malfunction without requiring data analysis by an experienced engineer, and can obtain countermeasures to address the cause, including changes to the operating procedure. Thus, according to the present invention, when an abnormality occurs in the battery storage system of an electric vehicle, it becomes possible to identify not only the location of the abnormality but also the cause of the abnormality, and to obtain countermeasures, including changes to the operating method.

[0094] [Example 2] In Example 1, the abnormality flag was treated as correct. However, in actual operation, the vehicle control device 13 and battery control device 21 may miscalculate the abnormality flag. Furthermore, if multiple abnormalities occur simultaneously, the abnormality flag does not represent all of them, and only outputs the abnormality flag corresponding to the first event that occurred or the event with the highest severity, resulting in some abnormal events not being recognized.

[0095] Therefore, in Example 2, the correctness of the abnormal flag is determined. The abnormality countermeasure system 30a of Example 2 differs from the abnormality countermeasure system 30 of Example 1 in that the correctness of the abnormal flag is determined before inputting it to the cause determination unit 31. Figure 16 is an operational block diagram of the abnormality countermeasure system 30a having a correctness determination unit 33 according to Example 2 of the present invention. Example 2 has the same structure and operation as Example 1, except for the parts of the abnormality countermeasure system 30a that are not specifically mentioned. In the following description, the same reference numerals are used for components that are the same as or equivalent to those in Example 1, and their descriptions are simplified or omitted.

[0096] In Example 2, the storage area 313 stores an abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw, and also stores correlation information between the abnormality flag Af and each of the data: battery data Db, vehicle data Dv, and weather data Dw.

[0097] The accuracy determination unit 33 continuously acquires the abnormality flag Af, battery data Db, vehicle data Dv, and weather data Dw according to a predetermined period. At this time, the accuracy determination unit 33 determines if any of the data among the battery data Db, vehicle data Dv, and weather data Dw has changed beyond a predetermined range, and detects if there has been a change but the abnormality indicated by the abnormality flag Af has not changed. At this time, the accuracy determination unit 33 determines that the abnormality flag Af is incorrect and outputs the accuracy determination information Ji for the abnormality flag.

[0098] Furthermore, the correct / incorrect determination unit 33, based on the correlation information in the storage area 313, determines that any of the battery data Db, vehicle data Dv, and weather data Dw has changed beyond a predetermined range, and extracts an anomaly flag corresponding to each of the three data: battery data Db, vehicle data Dv, and weather data Dw. If the anomaly flags extracted from the correlation information of each of the three data are common, the correct / incorrect determination unit 33 outputs a detailed anomaly flag sAf. The cause determination unit 31 calculates a cause weight table Wt based on the detailed anomaly flag sAf.

[0099] Furthermore, if the anomaly flags extracted from the correlation information of each of the three data sets are not common, the correct / incorrect determination unit 33 does not output the detailed anomaly flag sAf. When the cause determination unit 31 receives the correct / incorrect determination information Ji, it does not generate the cause weight table Wt. The cause determination unit 31 generates the cause weight table Wt based on the detailed anomaly flag sAf, battery data Db, vehicle data Dv, and weather data Dw sent in the next cycle.

[0100] (Effects / Actions) The configuration shown in Example 2 corrects errors in the abnormal flags and allows for the provision of countermeasures for all correct abnormal flags.

[0101] [Example 3] In Example 1, the proposed countermeasures are sent to the railway operator's control center 40, and the vehicle operation method and control program are switched manually. Meanwhile, the abnormality countermeasure system 30 is in communication with the vehicle control device 13 and the battery control device 21. In Example 3, the abnormality countermeasure system 30b then changes the control constants of the electric vehicle and the battery control device 21 in response to the proposed countermeasures.

[0102] Figure 17 is an operational block diagram of an abnormality countermeasure system 30b having a control constant changing unit 34 according to Embodiment 3 of the present invention. Embodiment 3 has the same structure and operation as Embodiment 1, except for the parts of the abnormality countermeasure system 30b that are not specifically indicated. In the following description, the same or equivalent components as those in Embodiment 1 described above are denoted by the same reference numerals, and their descriptions are simplified or omitted.

[0103] The control constant modification unit 34 outputs a modification signal Cs, which is a signal that instructs the vehicle control device 13 and the battery control device 21 to change their control constants. The control constant modification unit 34 takes the information regarding the proposed countermeasures from the countermeasure information Ci and the modification permission command Ps as input and outputs a modification signal Cs. The modification signal Cs is a signal that changes the control constants of the vehicle control device 13 and the battery control device 21 according to the content of the proposed countermeasures.

[0104] Here, the change signal Cs is usually a unique signal for each control unit, based on the specifications of the control unit. It is difficult for operators to use the change signal independently without the cooperation of the manufacturer, and changing it requires the effort of directly accessing the equipment on the vehicle. Not all countermeasures, such as hardware failures, can be addressed by changing control constants; only items managed by constants can be changed.

[0105] The change permission command Ps is a command that permits the control constant modification unit 34 to change the control constants in accordance with the proposed countermeasures. The change permission command Ps may be output at the railway operator's operation control center 40 after confirmation by the person in charge, or it may be output automatically when a proposed countermeasure information Ci is input from the abnormality countermeasures system 30b.

[0106] (Effects / Actions) With the configuration shown in Example 3, the railway operator's control center 40 can quickly and remotely implement measures to change control constants on the vehicle control device 13 and battery control device 21 on the vehicle.

[0107] [Example 4] In Example 1, the series of proposed countermeasures were implemented after an abnormality flag had been generated. On the other hand, many abnormality flags are triggered when a continuous quantity such as battery temperature exceeds a threshold, and future abnormality occurrence dates can be predicted from trends in past data. This method of diagnosing future abnormalities from existing data is called abnormality prediction diagnosis. If abnormalities are detected by abnormality prediction diagnosis before an abnormality flag is generated and countermeasures are implemented in advance, vehicle shutdowns due to abnormalities will not occur, and vehicle maintenance for countermeasures can be planned with ample lead time. Therefore, in Example 4, the abnormality countermeasure system 30c proposes countermeasures not by issuing an abnormality flag, but by issuing a predictive flag that anticipates the occurrence of an abnormality.

[0108] Figure 18 is an operational block diagram of an abnormality prevention system 30c having an abnormality prediction and diagnosis unit 35 according to Embodiment 4 of the present invention. Embodiment 4 has the same structure and operation as Embodiment 1, except for the parts of the abnormality prevention system 30c that are not specifically indicated. In the following description, the same reference numerals are used for components that are the same as or equivalent to those in Embodiment 1 described above, and their descriptions are simplified or omitted.

[0109] The cause determination unit anticipates the occurrence of an anomaly from the control device data and issues a warning flag. More specifically, the anomaly prediction diagnosis unit 35 has the function of detecting warning signs of an anomaly, and takes battery data Db, vehicle data Dv, weather data Dq, and past data Pd read from the storage area 313 as input to calculate the warning flag Sf. For example, in the case of an overtemperature anomaly, the unit plots the relationship between the highest daily battery temperature and the highest ambient temperature based on past data Pd, predicts future values, and determines that an anomaly has occurred if the threshold temperature is exceeded.

[0110] The warning flag Si is a flag that anticipates the occurrence of an anomaly and indicates signs of such an anomaly. For example, it is triggered when the expected date and time of the anomaly is within a threshold. The threshold is based on factors such as the time required to prepare countermeasures or the product's usage period.

[0111] (Effects / Actions) The configuration in Example 4 allows railway operators to detect signs of abnormalities through abnormality prediction diagnostics before an abnormality flag is generated. By implementing countermeasures in advance, train stoppages due to abnormalities will not occur, and train maintenance for these countermeasures can be planned with ample lead time.

[0112] [Example 5] In Example 1, the abnormality countermeasure system 30 transmits the cause analysis results, proposed countermeasures, and the effectiveness of the countermeasures to the railway operator's control center 40. On the other hand, in actual operation, when an abnormality flag is generated, the railway operator's control center 40 sends instructions for emergency measures to the driver's cab 12, and the crew who confirm the instructions carry out the emergency measures. Emergency measures include deciding whether to continue normal operation or to interrupt operation and move the vehicle to a station or depot, and deciding on life-extending measures corresponding to the abnormality flag. Among these, Patent Document 2 discloses an example in which the means of interrupting operation and moving the vehicle to a station or depot in response to the abnormality flag are notified. Example 5 differs from Patent Document 2 in that, firstly, it transmits to the driver's cab 12 a decision on whether operation can be continued even if an abnormality flag is generated, and secondly, a decision on life-extending measures corresponding to the abnormality flag.

[0113] The determination of whether operation can continue even if an abnormality flag is generated means instructing the driver's cab 12 that even if an abnormality flag is generated, the situation will not deteriorate seriously as long as the day's operating method is continued, and operation can continue. For example, in the case of overheating, if the battery overheating flag is generated after the peak temperature has passed, operation may be possible in subsequent operations because the temperature will decrease.

[0114] Furthermore, the decision on life-extending measures corresponding to an abnormal flag means that the abnormality countermeasure system 30d instructs the driver's cab 12 to implement immediately on a vehicle in operation from among the countermeasures proposed by the countermeasure proposal unit 32. For example, in the case of an overvoltage abnormality, it may be possible to continue using the vehicle by immediately adjusting the charge level up or down.

[0115] Figure 19 is an operational block diagram of an abnormality response system 30d having an emergency response suggestion unit 36 ​​according to Embodiment 5 of the present invention. Embodiment 5 has the same structure and operation as Embodiment 1, except for the parts of the abnormality response system 30d that are not specifically indicated. In the following description, the same or equivalent components as those in Embodiment 1 described above are denoted by the same reference numerals, and their descriptions are simplified or omitted.

[0116] The emergency response proposal unit 36 ​​has the function of determining either the operation continuation decision Co or the emergency response proposal Fa, and takes the cause weighting table Wt, cause analysis results, proposed countermeasures, and countermeasure information Ci including the effect of the countermeasures as input, and outputs the operation continuation decision Co and the emergency response proposal Fa to the railway operator's operation control center 40 and the driver's cab 12. The countermeasure proposal unit 32 uses a digital twin to predict the value of the target parameter after the abnormality flag is generated. The operation continuation decision Co instructs operation to continue if the predicted value of the target parameter is below the threshold for operation continuation decision, which is different from the abnormality flag issuance. In other words, if the abnormality countermeasure system 30d determines that the abnormality will not worsen by continuing operation, it instructs the driver's cab 12 to continue operation. The emergency response proposal Fa instructs the driver's cab 12 to take emergency measures to recover from the abnormality if, among the proposed countermeasures, the driver's cab 12 can immediately implement them and the operation can be continued as a result.

[0117] (Effects / Actions) With the configuration of Example 5, even if an abnormality flag is generated, if operation can be continued or emergency measures can be taken on the vehicle, the abnormality countermeasure system 30d can automatically instruct the driver's cab 12 and the railway operator's control center 40 to make a decision on continuing operation Co and propose emergency measures Fa, without requiring a decision from the railway operator's control center 40.

[0118] [Example 6] In Example 1, the proposed countermeasures, their effectiveness, and the cause-and-effect analysis output by the anomaly countermeasure system 30 were directly transmitted to the railway operator's control center 40. On the other hand, when actually implementing countermeasures, the manufacturer 50 will carry out replacements, repairs, and program changes other than vehicle operation. In addition, the proposed countermeasures, their effectiveness, and the cause-and-effect analysis output by the anomaly countermeasure system 30 are calculated automatically, and while it is not necessary to review their accuracy and decide whether to reflect them in the countermeasures, a certain level of expertise is required. Therefore, in Example 6, the proposed countermeasures, their effectiveness, and the cause-and-effect analysis output by the anomaly countermeasure system 30d are first transmitted to the manufacturer 50, and after verification and correction by the manufacturer 50, they are transmitted to the railway operator's control center 40.

[0119] Figure 20 is an operational block diagram of the anomaly detection system 30e transmitted to the manufacturer 50 according to Embodiment 6 of the present invention. Embodiment 6 has the same structure and operation as Embodiment 1, except for the parts of the anomaly detection system 30e that are not specifically indicated. In the following description, the same or equivalent components as those in Embodiment 1 are denoted by the same reference numerals, and their descriptions are simplified or omitted.

[0120] Here, the countermeasure information Ci, including proposed countermeasures, the effectiveness of the countermeasures, and cause-and-effect analysis, is first sent to the manufacturer 50, and after verification and correction at the manufacturer 50, it is sent to the train control center 40, but is not limited to this. The proposed countermeasures, the effectiveness of the countermeasures, and the cause-and-effect analysis output by the abnormality countermeasure system 30e may be directly sent in parallel to the train control center 40 of the railway operator and the manufacturer 50, and the manufacturer 50 may send the judgment result of the transmitted content to the train control center 40.

[0121] (Effects / Actions) With the configuration of Example 6, the train control center 40 of the railway operator can delegate to the manufacturer 50 the accuracy of the cause-and-effect analysis, countermeasure proposals, and countermeasure effectiveness output by the abnormality countermeasure system 30e without having to judge it itself. Alternatively, instead of the manufacturer 50, a railway incident operator or a person entrusted by the manufacturer 50 with the management of all or part of the railway vehicles may receive the countermeasure proposals and implement the various countermeasures.

[0122] [Example 7] In Example 1, the proposed countermeasures when an abnormality flag was generated were applied to the train set containing the vehicle in which the abnormality was detected. On the other hand, multiple train sets are introduced on a given railway line. These are generally manufactured and used at roughly the same time, and their usage is standardized through rotation, so abnormalities often occur at the same time. Therefore, if an abnormality occurs in one train set, it is necessary to investigate whether an abnormality may occur in other train sets and when such an abnormality is likely to occur.

[0123] Figure 21 is an operational block diagram of an abnormality countermeasure system 30f having a ripple effect evaluation unit 37 according to Embodiment 6 of the present invention. Embodiment 6 has the same structure and operation as Embodiment 1, except for the parts of the abnormality countermeasure system 30f that are not specifically indicated. In the following description, the same or equivalent components as those in Embodiment 1 are denoted by the same reference numerals, and their descriptions are simplified or omitted.

[0124] The abnormality countermeasure system 30f analyzes data from one or both of the vehicle control devices and battery control devices of electric vehicles other than the one that experienced the abnormality flag Af in an electric vehicle, diagnoses signs of an abnormality occurring in electric vehicles other than the one that experienced the abnormality, and notifies the railway operator. Specifically, consider a situation where there are two or more arbitrary number of vehicle drive systems 1A, and one of the drive systems 1A experiences an abnormality. The abnormality countermeasure system 30f of Example 6 has a cause determination unit 31 and a countermeasure proposal unit 32 in addition to the cause determination unit 31 and the countermeasure proposal unit 32 of Example 1, as well as a ripple effect evaluation unit 37 and a storage area 313. Note that the storage area 313 may be installed as one in the abnormality countermeasure system 30f and used in common by the cause determination unit 31 and the countermeasure proposal unit 32, or it may be installed in the cause determination unit 31 and the countermeasure proposal unit 32 respectively. Furthermore, the anomaly countermeasure system 30f in Example 6 is usually installed as a server outside the drive system 1A due to the need to analyze data from other train sets. However, it is also possible to install the anomaly countermeasure system 30f in each train set and receive anomaly flags and cause weight tables Wt from other train sets via communication. The storage area 313 stores anomaly flags Af, battery data Db, vehicle data Dv, and weather data Dw from multiple drive systems 1A.

[0125] The propagation evaluation unit 37 takes the abnormality flag and cause weight table Wt of the configuration in which the abnormality occurred, and past data of other configurations as input, and outputs a precursor flag Si and an expected abnormality date and time Es that indicate the possibility of the abnormality occurring in other configurations. The propagation evaluation unit has the function of calculating whether there are precursors to the same abnormality occurring in configurations other than the one in which the abnormality occurred, and the expected date and time of such an abnormality occurrence. The diagnosis of precursors to abnormalities in other configurations and the date and time of occurrence can be predicted from the temporal evolution of a target parameter for a certain abnormality flag, for example, as processed by the abnormality precursor diagnosis unit 35 in Example 4. In addition, it is possible to make a determination based on how close the parameter corresponding to the abnormality weight calculated by the cause determination unit 31 is to that of the configuration in which the abnormality occurred in other configurations.

[0126] (Effects / Actions) With the configuration of Example 7, the railway operator's control center 40 can automatically determine if an abnormality flag is generated in one train set, as well as the occurrence of abnormalities in other train sets and the date of the abnormalities, enabling it to quickly plan maintenance work in advance.

[0127] [Example 8] Figure 22 shows the configuration of a drive system for a railway vehicle equipped with a battery to which the abnormality countermeasure system according to Embodiment 8 of the present invention is applied. Embodiment 8 is a specific configuration when the abnormality countermeasure system 30 is located outside the vehicle, and has the same structure and operation as Embodiment 1 except for the parts that are not specifically indicated. In the following description, the same or equivalent components as those in Embodiment 1 described above are denoted by the same reference numerals, and their descriptions are simplified or omitted. The data transmission / reception device 60 is located inside the vehicle and communicates with the battery unit 20, the vehicle control unit 13, and the driver's cab 12, etc. The data transmission / reception device 60 also communicates wirelessly with an abnormality detection system 30 located outside the vehicle, for example, on a server 70 owned by the manufacturer or on the cloud. Wireless communication is, for example, communication via the internet and the cloud using a mobile phone line, but is not limited to this, and may also be an intranet using local 5G. The data transmission / reception device 60 does not necessarily have to be independent of the drive system 1A and may be included in the vehicle control unit 13 or the battery control unit 21. Alternatively, the abnormality detection system may collect information directly without going through the vehicle control unit 13. Furthermore, sensors such as GPS and ammeters may be installed in the data transmission / reception device 60, and the status may be inferred from the measurement results. The abnormality response system 30 communicates with the train control center 40, for example, via the internet, but is not limited to this; it may also communicate via a dedicated line, for example. Furthermore, weather data Dw, etc., measured at a nearby location outside the vehicle may be obtained directly without going through the vehicle control device 13.

[0128] (Effects / Actions) The configuration of Example 8 reduces the number of devices inside the vehicle, making it easier for the anomaly countermeasure system 30 to collect various data. This allows for the efficient construction of a system for proposing countermeasures, and enables more effective countermeasure proposals.

[0129] Although embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of the invention. [Explanation of Symbols]

[0130] 1A…Drive System 1B…Drive System 2…Pantograph 3…Engine 4…Generator 5…Converter 6…Inverter for electric motors 7...Electric motor 8...Reducer 9…Wheel axle 10…Inverter for auxiliary equipment 11... Auxiliary equipment 12... Driver's cab 13... Vehicle control system 14… Overhead lines 20...Battery storage device 21...Battery control device 30, 30a, 30b, 30c, 30d, 30e, 30f... Anomaly response system 31…Cause determination section 311... Related parameter extraction unit 312...Cause weight calculation unit 313…Save area 314...Model Verification Department 3141…Judgment section 3142... Vehicle Digital Twin 3143...Battery-powered digital twin 31431...Calculation target specification section 31432…Temperature Model 31433…Closed-circuit voltage model 31434…Charging rate model 315...Sensitivity calculation unit 3151...Sensitivity map generation unit 3152...Micro-change generation unit 32… Countermeasure Proposal Department 321... Calculation unit for changeable range 322...Countermeasure Effectiveness Calculation Unit 323…Proposal decision department 33... Correct / Incorrect Judgment Unit 34...Control constant modification section 35... Department for Diagnosing Abnormalities 36…Emergency Measures Proposal Department 37… Impact Assessment Department 40... Train dispatch center 50... Manufacturer 70... Server

Claims

1. In an abnormality countermeasure system used in an electric vehicle equipped with a drive system using a battery storage device, The aforementioned abnormality countermeasure system includes a cause determination unit and a countermeasure proposal unit. The aforementioned cause determination unit, The status of the electric vehicle and the battery storage device is monitored, If an abnormality flag is output from at least one of the electric vehicle and the battery storage device, The state of at least one of the electric vehicle and the battery device is analyzed, and the cause of the abnormality indicated by the abnormality flag is identified. The aforementioned countermeasure proposal department, The electric vehicle manager will be notified of the results of the cause analysis of the abnormal flag and proposed countermeasures. The proposed countermeasures include categories such as changing the operation method of the electric vehicle, changing the control parameters of the battery device or the drive system, repairing faulty software or equipment in the battery device or the drive system, and replacing deteriorated batteries. An anomaly response system characterized by the following:

2. In the abnormality countermeasure system according to claim 1, The proposed countermeasures include a distinction between changing the operating method or changing the control parameters under special conditions. An anomaly response system characterized by the following:

3. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit subdivides the cause of the abnormal flag and presents each cause with a weight indicating the degree of its influence. An anomaly response system characterized by the following:

4. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit checks the history of parameters related to the abnormal flag and assigns weights to parameters that have changed significantly in the history. An anomaly response system characterized by the following:

5. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit checks the history of the parameters related to the abnormal flag, and uses the ratio of past parameter values ​​to the latest parameter values ​​as a weight in the history. An anomaly response system characterized by the following:

6. In the abnormality countermeasure system according to claim 1 or 2, The aforementioned cause determination unit, It has a vehicle digital twin and a battery digital twin, The sensitivity of the abnormal flag to the target parameter is calculated by slightly changing the parameter related to the abnormal flag, and the weight of the cause of the abnormal flag is calculated. An anomaly response system characterized by the following:

7. In the abnormality countermeasure system according to claim 6, The aforementioned battery digital twin calculates the battery temperature, closed-circuit voltage, and charge level. An anomaly response system characterized by the following:

8. In the abnormality countermeasure system according to claim 1 or 2, The abnormality countermeasure system responds to the abnormality flag generated in the electric vehicle, By analyzing data from one or both of the vehicle control device and battery control device of an electric vehicle different from the aforementioned electric vehicle, Diagnosing signs of malfunction in electric vehicles other than the one that experienced the malfunction, Notify the aforementioned administrator. An anomaly response system characterized by the following:

9. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit classifies the cause of the abnormal flag into one of the following: (1) sudden event, (2) abnormal environment, (3) harsh vehicle operation method, (4) battery degradation, (5) inappropriate control parameters, (6) control program malfunction, or (7) hardware failure. An anomaly response system characterized by the following:

10. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit subdivides and presents the cause of the abnormal flag using fault tree analysis. An anomaly response system characterized by the following:

11. In the abnormality countermeasure system according to claim 1 or 2, The countermeasure proposal unit proposes multiple countermeasures for each individual cause of the abnormal flag. An anomaly response system characterized by the following:

12. In the abnormality countermeasure system according to claim 1 or 2, The countermeasure proposal unit sets the changeable range of the parameter related to the abnormal flag, The proposed parameter change values ​​are set within the aforementioned changeable range. An anomaly response system characterized by the following:

13. In the abnormality countermeasure system according to claim 12, The countermeasure proposal unit sets the modifiable range based on whether the deterioration of the target parameters of abnormal flags other than the abnormal flag is within a threshold. An anomaly response system characterized by the following:

14. In the abnormality countermeasure system according to claim 1 or 2, The countermeasure proposal unit modifies the control constants of the electric vehicle or the battery storage device. An anomaly response system characterized by the following:

15. In the abnormality countermeasure system according to claim 1 or 2, The cause determination unit anticipates the occurrence of an abnormality from the control device data and issues a warning flag. An anomaly response system characterized by the following:

16. In the abnormality countermeasure system according to claim 1 or 2, The abnormality countermeasure system instructs the driver's cab to select from the countermeasures proposed by the countermeasure proposal unit that can be implemented in the electric vehicle while it is in operation. An anomaly response system characterized by the following:

17. In a method for addressing malfunctions in electric vehicles equipped with a drive system using a battery storage device, The state of the electric vehicle and the battery device is monitored, and if an abnormality flag is output from at least one of the electric vehicle and the battery device, the state of at least one of the electric vehicle and the battery device is analyzed to identify the cause of the abnormality indicated by the abnormality flag. The electric vehicle manager will be notified of the results of the cause analysis of the abnormal flag and proposed countermeasures. The proposed countermeasures include categories such as changing the operation method of the electric vehicle, changing the control parameters of the battery device or the drive system, repairing faulty software or equipment in the battery device or the drive system, and replacing the battery. An abnormality countermeasure method characterized by the following.

Images